Developers
July 7, 2020

How Unity Uses GCP BigQuery To Analyze Petabytes of Data

Unity, the worldwide leader of gaming, migrates its infrastructure to Google Cloud.

In this era, more and more companies are moving their data, applications, and services to the cloud. However, migrating existing applications to the cloud can be challenging. Unity, the biggest gaming development platform, has shared their experience and insights on how they made this happen.

The company not only works on gaming but on architecture, film, financial operations, customer success, marketing, and others. The company counts with huge amounts of data, and to be able to manage it correctly, they must use a service.  

Unity uses and has used many data services. The idea of the use of these platforms is to centralize data and ensure that they can focus on their customers efficiently. They now use Google Cloud.

The journey of Moving to Google Cloud

Before moving to Google Cloud, the company used a solution that stored data as datasets. This data was later used for machine learning. The company also used a data warehouse to host their enterprise data, and another solution to process reports from the input of streaming data. 

The impact of centralizing their data helped the company manage its teams better. Instead of many teams working on the data, they have a specific unit for it. After building a centralized data service, it can be used multiple times and for multiple purposes. Data remains secured and private.  

By employing self-service tools, the centralized data services teams can work following a strategy to build an environment that gives independence. The independence of teams within a company is one of the crucial things that can occur. It makes the company flexible and more productive.

The platform is easy to use, so the data users can manage their data in a way they can understand. High standards are not lost, to the contrary, they are incremented. The aim of the company by developing this service is to connect the data to business with machine learning.

The migration from the past service to Google Cloud happened two years ago. It was not easy as they had tons of data, but they don't regret it. Data migration of a company is a very sensitive process, as there is always the nerves of losing data or data being filtered. The fact that they moved it to Google Cloud allowed them to do an infrastructure migration without worries.

BigQuery, the chosen service, but why?

The analytics of the infrastructure is based on BigQuery. They chose this service because of its potential to scale, because of its features that support multiple inputs, because of its happy users and because of the security and privacy provided.

As you can imagine, being the leader of any industry comes with immense amounts of data. There are more than 3 billion downloads of apps per month. Yes, you read right, billions!

The system supports gamers worldwide, becoming one of the largest advertising networks in the world. The systems have to be highly efficient, as the requests add up to tens of billions every day.

The system processes petabytes of data per month, based on this processed data, the business bases its operations and decisions. It is always a good practice to base your business decision making on data. Data will never lie it will show you exactly what is going on.  

The captured data is not only passed to the CEO, and decision-taking people, but also used by all the teams within the company. Product managers are an example of who need to understand data the most. By knowing how the production adapts to the consumer, the PM knows where to put the focus on the next iteration.

The marketing team has to understand and digest data. Their understanding of how the market is responding to the company's offers is directly proportional to how they go to market in the future.

The data is accessible by their employees, but the datasets are restricted and private. The company has chosen to make the data anonymous and encrypted. This allows the company to use the data, while the core of the data is protected.

The chosen data platform had to support machine learning, as it is used widely within the company. This was a requirement and not something wanted, as the company considered it a need.

The machine learning is based on fast feedback loops. The services generate the needed data, and then the data is read by the service for optimization. One very common example is to provide recommendations on learning material, leading to better user experience.

In conclusion, Unity has migrated its entire infrastructure to Google Cloud. Unity is the worldwide leader of gaming and influences other industries too. Being a leader means having the capacity to administer data correctly. The company handles Billions of downloads every month and counts with tens of billions of data. To analyze their data and understand their strategies better, they use machine learning and they base it on BigQuery.

TagsGCPBigQueryMachine LearningData Analysis
Lucas Bonder
Technical Writer
Lucas is an Entrepreneur, Web Developer, and Article Writer about Technology.

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DevelopersJuly 7, 2020
How Unity Uses GCP BigQuery To Analyze Petabytes of Data
Unity, the worldwide leader of gaming, migrates its infrastructure to Google Cloud.

In this era, more and more companies are moving their data, applications, and services to the cloud. However, migrating existing applications to the cloud can be challenging. Unity, the biggest gaming development platform, has shared their experience and insights on how they made this happen.

The company not only works on gaming but on architecture, film, financial operations, customer success, marketing, and others. The company counts with huge amounts of data, and to be able to manage it correctly, they must use a service.  

Unity uses and has used many data services. The idea of the use of these platforms is to centralize data and ensure that they can focus on their customers efficiently. They now use Google Cloud.

The journey of Moving to Google Cloud

Before moving to Google Cloud, the company used a solution that stored data as datasets. This data was later used for machine learning. The company also used a data warehouse to host their enterprise data, and another solution to process reports from the input of streaming data. 

The impact of centralizing their data helped the company manage its teams better. Instead of many teams working on the data, they have a specific unit for it. After building a centralized data service, it can be used multiple times and for multiple purposes. Data remains secured and private.  

By employing self-service tools, the centralized data services teams can work following a strategy to build an environment that gives independence. The independence of teams within a company is one of the crucial things that can occur. It makes the company flexible and more productive.

The platform is easy to use, so the data users can manage their data in a way they can understand. High standards are not lost, to the contrary, they are incremented. The aim of the company by developing this service is to connect the data to business with machine learning.

The migration from the past service to Google Cloud happened two years ago. It was not easy as they had tons of data, but they don't regret it. Data migration of a company is a very sensitive process, as there is always the nerves of losing data or data being filtered. The fact that they moved it to Google Cloud allowed them to do an infrastructure migration without worries.

BigQuery, the chosen service, but why?

The analytics of the infrastructure is based on BigQuery. They chose this service because of its potential to scale, because of its features that support multiple inputs, because of its happy users and because of the security and privacy provided.

As you can imagine, being the leader of any industry comes with immense amounts of data. There are more than 3 billion downloads of apps per month. Yes, you read right, billions!

The system supports gamers worldwide, becoming one of the largest advertising networks in the world. The systems have to be highly efficient, as the requests add up to tens of billions every day.

The system processes petabytes of data per month, based on this processed data, the business bases its operations and decisions. It is always a good practice to base your business decision making on data. Data will never lie it will show you exactly what is going on.  

The captured data is not only passed to the CEO, and decision-taking people, but also used by all the teams within the company. Product managers are an example of who need to understand data the most. By knowing how the production adapts to the consumer, the PM knows where to put the focus on the next iteration.

The marketing team has to understand and digest data. Their understanding of how the market is responding to the company's offers is directly proportional to how they go to market in the future.

The data is accessible by their employees, but the datasets are restricted and private. The company has chosen to make the data anonymous and encrypted. This allows the company to use the data, while the core of the data is protected.

The chosen data platform had to support machine learning, as it is used widely within the company. This was a requirement and not something wanted, as the company considered it a need.

The machine learning is based on fast feedback loops. The services generate the needed data, and then the data is read by the service for optimization. One very common example is to provide recommendations on learning material, leading to better user experience.

In conclusion, Unity has migrated its entire infrastructure to Google Cloud. Unity is the worldwide leader of gaming and influences other industries too. Being a leader means having the capacity to administer data correctly. The company handles Billions of downloads every month and counts with tens of billions of data. To analyze their data and understand their strategies better, they use machine learning and they base it on BigQuery.

GCP
BigQuery
Machine Learning
Data Analysis
About the author
Lucas Bonder -Technical Writer
Lucas is an Entrepreneur, Web Developer, and Article Writer about Technology.

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